Evaluasi Program Penyediaan Air Minum Dan Sanitasi Berbasis Masyarakat (Pamsimas) Di Kecamatan Tembalang

Pembangunan tidak lain merupakan suatu proses Perubahan yang berlangsung secara sadar, terencana dan berkelanjutan dengan sasaran utamanya adalah untuk meningkatkan kesejahteraan hidup manusia. Salah satu pembangunan yang menjadi perhatian adalah kebutuhan air bersih dan sanitasi. Pamsimas merupakan salah satu bentuk solusi dari kurangnya air bersih dan sanitasi di Indonesia. Tetapi pelaksanaan Pamsimas masih belum optimal, tidak terkecuali pelaksanaan di Kecamatan Tembalang Kota Semarang. Tujuan dari penelitian ini adalah untuk mengevaluasi Program Penyediaan Air Minum dan Sanitasi Berbasis Masyarakat (Pamsimas) di Kecamatan Tembalang. Dalam evaluasi ini digunakan enam kriteria evaluasi yaitu efisiensi, efektivitas, kecukupan, perataan, responsivitas dan ketepatan. Hasil penelitian ini menunjukkan bahwa Pamsimas di Kecamatan Tembalang sudah efektif dalam memenuhi kebutuhan air bersih dan sanitasi. Tetapi masih ditemui kekurangan, seperti kurang meratanya pembangunan tower air dan perpipaan Pamsimas, selain tidak merata masih banyak warga miskin yang masih belum dapat mendapatkan Pamsimas. Rekomendasi untuk meningkatkan perataan pembangunan Pamsimas dapat dilakukan pada saat musyawarah penentuan prioritas pembangunan dilakukan oleh seluruh elemen pelaksana Pamsimas, hal ini agar tercipta keadilan dan pemerataan pada pembangunan karena diawasi langsung oleh semua pihak yang terkait

Taxonomy distribution of genes in the training dataset.

<p>Taxonomy distribution of genes in the training dataset.</p

Automatic Assignment of Prokaryotic Genes to Functional Categories Using Literature Profiling

<div><p>In the last years, there was an exponential increase in the number of publicly available genomes. Once finished, most genome projects lack financial support to review annotations. A few of these gene annotations are based on a combination of bioinformatics evidence, however, in most cases, annotations are based solely on sequence similarity to a previously known gene, which was most probably annotated in the same way. As a result, a large number of predicted genes remain unassigned to any functional category despite the fact that there is enough evidence in the literature to predict their function. We developed a classifier trained with term-frequency vectors automatically disclosed from text <em>corpora</em> of an ensemble of genes representative of each functional category of the J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR) ontology. The classifier achieved up to 84% precision with 68% recall (for confidence≥0.4), F-measure 0.76 (recall and precision equally weighted) in an independent set of 2,220 genes, from 13 bacterial species, previously classified by JCVI-CMR into unambiguous categories of its ontology. Finally, the classifier assigned (confidence≥0.7) to functional categories a total of 5,235 out of the ∼24 thousand genes previously in categories “Unknown function” or “Unclassified” for which there is literature in MEDLINE. Two biologists reviewed the literature of 100 of these genes, randomly picket, and assigned them to the same functional categories predicted by the automatic classifier. Our results confirmed the hypothesis that it is possible to confidently assign genes of a real world repository to functional categories, based exclusively on the automatic profiling of its associated literature. The LitProf - Gene Classifier web server is accessible at: <a href="http://www.cebio.org/litprofGC">www.cebio.org/litprofGC</a>.</p> </div

Examples of genes classified by LitProf- Gene Classifier and further validated by manually reviewing their literature.

*<p>The GO terms from Biological Process, Molecular Function and Cellular Component ontologies associated with each gene in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047436#pone-0047436-t004" target="_blank">table 4</a> (or, in most cases, their prokaryotic orthologs) were retrieved from AmiGO (<a href="http://amigo.geneontology.org" target="_blank">http://amigo.geneontology.org</a>) by querying the database with their canonical gene names. In most cases the GO terms retrieved supported the functional categorization predicted by LitProf – Gene Classifier, although there is not an exact correspondence between GO and JCVI-CMR ontologies. Six gene names out 16 tested had no match in AmiGO.</p

Recall vs. precision of the classifier.

<p>The red line represents the average performance of the initial classifier trained with the original categories of the JCVI-CMR ontology. The blue line, represents the average performance of the final classifier trained with a rearranged version of the ontology where noisy subcategories were merged together to create the Mix Category. For red and blue lines, the average was calculated from 100 replicates of 10-fold cross validation. The green line represents the performance of the final classifier in an independent gene set. Horizontal bars represent the standard deviations of recall. The dashed lines represent the standard deviation of precision for the blue curve.</p

Gene distribution in the functional categories of the JCVI-CMR ontology.

<p>Only categories used to train the classifier are shown. Mix category regroups the noisy subcategories. The original column refers to the complete J. Craig Venter Institute Comprehensive Microbial Resource (JCVI-CMR). The training column refers to the dataset used to train the classifier. The classified column refers to the “Unknown function” and “Unclassified” genes that were classified by LitProf- Gene Classifier with confidence≥0.7. There is no significant difference between the original and training datasets (p>0.05 in paired t-test; confidence level of 95%).</p

Summary of the classification of genes previously assigned to categories “Unknown function” and “Unclassified” of the JCVI-CMR ontology.

<p>From the total number of “Unknown function” and “Unclassified” genes, nearly 50% have a name, with is crucial for text <i>corpora</i> retrieval. From those, ∼70% have enough literature (min = five abstracts; max = 50) for classification, and in this group, ∼22% could be assigned by LitProf - Gene Classifier to a functional category with high confidence.</p><p>JCVI-CMR = J. Craig Venter Institute Comprehensive Microbial Resource.</p

Number of genes assigned to functional categories with different confidence thresholds.

<p>Number of genes assigned to functional categories with different confidence thresholds.</p

Additional file 1: of PIPEBAR and OverlapPER: tools for a fast and accurate DNA barcoding analysis and paired-end assembly

PIPEBAR and OverlapPER’s usage. Here we show all the instructions for the installation of Pipebar and all the commands used in the tests made with Pipebar, OverlapPER and the other tools used as benchmark. (PDF 639 kb

Biomarker panels for characterizing microbial community biofilm formation as composite molecular process

<div><p>Microbial consortia execute collaborative molecular processes with contributions from individual species, on such basis enabling optimized molecular function. Such collaboration and synergies benefit metabolic flux specifically in extreme environmental conditions as seen in acid mine drainage, with biofilms as relevant microenvironment. However, knowledge about community species composition is not sufficient for deducing presence and efficiency of composite molecular function. For this task molecular resolution of the consortium interactome is to be retrieved, with molecular biomarkers particularly suited for characterizing composite molecular processes involved in biofilm formation and maintenance. A microbial species set identified in 18 copper environmental sites provides a data matrix for deriving a cross-species molecular process model of biofilm formation composed of 191 protein coding genes contributed from 25 microbial species. Computing degree and stress centrality of biofilm molecular process nodes allows selection of network hubs and central connectors, with the top ranking molecular features proposed as biomarker candidates for characterizing biofilm homeostasis. Functional classes represented in the biomarker panel include quorum sensing, chemotaxis, motility and extracellular polysaccharide biosynthesis, complemented by chaperones. Abundance of biomarker candidates identified in experimental data sets monitoring different biofilm conditions provides evidence for the selected biomarkers as sensitive and specific molecular process proxies for capturing biofilm microenvironments. Topological criteria of process networks covering an aggregate function of interest support the selection of biomarker candidates independent of specific community species composition. Such panels promise efficient screening of environmental samples for presence of microbial community composite molecular function.</p></div